Using Elastic Facies to Perform Quality Control of Well Logs

ABSTRACT

Methods and systems for performing log quality control on well data of non-key wells is provided. A method of identifying elastic facies in non-key wells as part of Log Quality Control (LQC) includes selecting one or more key wells, building a reference model of elastic facies using the well log data of the selected one or more key wells, propagating the reference model to well data of one or more non-key wells, benchmarking the well data of the non-key wells with the reference model, and calibrating the well data of the non-key wells with the reference model.

CLAIM OF PRIORITY

This application claims priority under 35 U.S.C. § 119(e) to U.S. PatentApplication Ser. No. 62/813,646, filed on Mar. 4, 2019, the entirecontents of which are hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure relates to performing quality control of welllogs and, more particularly, to using elastic facies to perform qualitycontrol of well logs.

BACKGROUND

Performing a quality control analysis of well logs, referred to as LogQuality Control (“LQC”), is an important step for any rock physics studyor seismic inversion study or both. Good quality log data ensures thesuccess of any inversion study. The LQC becomes challenging when the logdata associated with different wells are acquired in different ways orusing different tool types or tools of different vintages, for example.When working with seismic data, a zone of interest along a depth of theearth is generally larger than a hydrocarbon producing zone or zones.

There are multiple challenges in an LQC process. For example, differenttool types and vintages, log data acquired by different servicecompanies, variability in logging suites, and different vertical wellpenetrations pose challenges to qualifying well logs. An LQC processbecomes even more challenging when an area covered by a reservoir orreservoirs and an areal coverage representing lithological variationswithin the reservoir or reservoirs encompasses a large number of wells.For example, an LQC project may involve a project that encompasseshundreds or even thousands of well. Consequently, such an LQC processmay involve a sizable amount of log data. The preparation and analysisof such an amount of log data involves a vast amount of time and labor.

Conventionally, log calibration involves selecting zones of knownlithologies having minimum fluid effects, such as thick anhydriteintervals or clean water bearing sand or limestone intervals, and usingthese zones to benchmark log readings for RHOB (Density), DT-P(P-Sonic), DT-S(Shear Sonic), GR (Gamma Ray), etc. These benchmarkvalues are used to calibrate other wells in an area of interest.

SUMMARY

The proposed processes for using elastic facies to perform qualitycontrol of well logs is divided into two main parts. In the first part,the elastic facies are predicted from key wells using Heterogeneous RockAnalysis technique (HRA), which uses the Principal Component Analysis(PCA) to classify rocks. The key wells are selected based on the loggingsuite, the year of logging, a quality of log data, the reservoircoverage, and the areal coverage representing the lithologicalvariations within the reservoirs. The predicted facies are calibratedusing different petrophysical properties, well-by-well. Multi-wellcross-plot techniques, such as Vp/Vs versus Acoustic Impedancecolor-coded by reservoirs properties, including water saturation,porosity and volume of shale, are utilized to identify the cleanhydrocarbon-bearing sands within the wells. The reservoir intervalsidentified by these particular facies are then validated with formationtest data from wells.

In the second part, the HRA facies are used as a discriminating tool tobenchmark the log readings for the log validation process. The mean andthe mode log values for the Gamma Ray, the Density, the NeutronPorosity, and the P- and S-Sonic are established for each facies. Thesebenchmark values are then used as guides to calibrate and condition logsfrom nearby wells which are either logged with old tool vintages or thewireline data is severely affected by borehole washouts or gas kicks.This tool is also helpful to constrain the model to estimate shear anddensity logs over small missing intervals, although the proper rockphysics model is used to predict missing shear wave and/or density datain the second phase.

The one or more embodiments of the present disclosure provide one ormore of the following advantages. The processes described in thisdisclosure provide an accurate and consistent approach to LQC andcondition logs across a given environment. This process reduces oreliminates a need of providing well marker data and a petrophysicalanalyses at a time of log data preparation phase and minimize the errorsin the LQC process introduced by inconsistencies in the marker data. Theproposed workflow accelerates and debottlenecks the LQC process for megaprojects which involves thousands of wells with tight project deadlines

In a general aspect, a process for identifying elastic facies in non-keywells as part of Log Quality Control (LQC) includes the actions ofselecting one or more key wells. The actions include building areference model of elastic facies using the well log data of theselected one or more key wells. The actions include propagating thereference model to well data of one or more non-key wells. The actionsinclude benchmarking the well data of the non-key wells with thereference model. The actions include and calibrating the well data ofthe non-key wells with the reference model.

In some implementations, building the reference model includesperforming a principal Component Analysis (PCA) on the well log data ofthe one or more key wells.

In some implementations, the one or more key wells are selectedaccording to one or more of: a level of penetration of the well logdata, a period of time in which the well log data is captured, aresolution of the well log data, a reservoir coverage of the well logdata, and an areal coverage representing lithological variations withina well that is represented by the well log data.

In some implementations, building the reference model includespredicting elastic facies of a key well.

In some implementations, the actions include validating the predictedelastic facies using a plurality of cross-plots, each cross-plotincluding different elastic attributes from other cross-plots of theplurality.

In some implementations, each elastic facies is represented by a datacluster from a critical path analysis (CPA). The CPA is furtherconfigured to provide, for each elastic facies, a mean value of eachinput data type corresponding to that elastic facie to distinguish thatelastic facie from other elastic facies. In some implementations, theactions include generating, in response to calibrating the well data ofthe non-key wells with the reference model, an alert when the well dataof the non-key well is outside of a range of values indicated by thereference model.

In a general aspect, a system for identifying elastic facies in non-keywells as part of Log Quality Control (LQC) includes a memory for storingone or more instructions and one or more processing devices incommunication with the memory and configured to execute the one or moreinstructions to perform operations. Generally, the operations includeselecting one or more key wells. The operations include building areference model of elastic facies using the well log data of theselected one or more key wells. The operations include propagating thereference model to well data of one or more non-key wells. Theoperations include benchmarking the well data of the non-key wells withthe reference model. The operations include calibrating the well data ofthe non-key wells with the reference model.

In some implementations, building the reference model includesperforming a principal Component Analysis (PCA) on the well log data ofthe one or more key wells.

In some implementations, the one or more key wells are selectedaccording to one or more of: a level of penetration of the well logdata, a period of time in which the well log data is captured, aresolution of the well log data, a reservoir coverage of the well logdata, and an areal coverage representing lithological variations withina well that is represented by the well log data.

In some implementations, building the reference model includespredicting elastic facies of a key well.

In some implementations, the actions include validating the predictedelastic facies using a plurality of cross-plots, each cross-plotincluding different elastic attributes from other cross-plots of theplurality.

In some implementations, each elastic facies is represented by a datacluster from a critical path analysis (CPA). The CPA is furtherconfigured to provide, for each elastic facies, a mean value of eachinput data type corresponding to that elastic facie to distinguish thatelastic facie from other elastic facies. In some implementations, theactions include generating, in response to calibrating the well data ofthe non-key wells with the reference model, an alert when the well dataof the non-key well is outside of a range of values indicated by thereference model.

In a general aspect, one or more non-transitory computer readable mediastore instructions that are executable by one or more processorsconfigured to perform operations for identifying elastic facies innon-key wells as part of Log Quality Control (LQC). Generally, theoperations include selecting one or more key wells. Generally, theoperations include building a reference model of elastic facies usingthe well log data of the selected one or more key wells. Generally, theoperations include propagating the reference model to well data of oneor more non-key wells. Generally, the operations include benchmarkingthe well data of the non-key wells with the reference model. Generally,the operations include calibrating the well data of the non-key wellswith the reference model.

In some implementations, building the reference model includesperforming a principal Component Analysis (PCA) on the well log data ofthe one or more key wells.

In some implementations, the one or more key wells are selectedaccording to one or more of: a level of penetration of the well logdata, a period of time in which the well log data is captured, aresolution of the well log data, a reservoir coverage of the well logdata, and an areal coverage representing lithological variations withina well that is represented by the well log data.

In some implementations, building the reference model includespredicting elastic facies of a key well.

In some implementations, the actions include validating the predictedelastic facies using a plurality of cross-plots, each cross-plotincluding different elastic attributes from other cross-plots of theplurality.

In some implementations, each elastic facies is represented by a datacluster from a critical path analysis (CPA). The CPA is furtherconfigured to provide, for each elastic facies, a mean value of eachinput data type corresponding to that elastic facie to distinguish thatelastic facie from other elastic facies. In some implementations, theactions include generating, in response to calibrating the well data ofthe non-key wells with the reference model, an alert when the well dataof the non-key well is outside of a range of values indicated by thereference model.

The details of one or more embodiments of the present disclosure are setforth in the accompanying drawings and the description below. Otherfeatures, objects, and advantages of the present disclosure will beapparent from the description and drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a flowchart of an example process, according to someimplementations of the present disclosure.

FIG. 2 is an example data cluster chart and associated key that are theresult of the PCA analyses, according to some implementations of thepresent disclosure.

FIGS. 3A-3B show example charts that show well log data input into aCritical Path Analysis (CPA) and a continuous facies output from the CPAanalysis, according to some implementations of the present disclosure.

FIGS. 4A-4B show facies comparisons with petrophysical analyses fornon-key wells, according to some implementations of the presentdisclosure.

FIGS. 5A-5B show a series of histograms for selected key wells for datatypes DT-P, DTSM, GR, and RHOB, respectively, according to someimplementations of the present disclosure.

FIG. 6 shows histograms for DT-P data for six different non-key wells,according to some implementations of the present disclosure.

FIGS. 7A-7C show final predicted facies for a key well in a study area,according to some implementations of the present disclosure.

FIGS. 8A-8D show Vp/Vs ratio versus AI cross-plots, according to someimplementations of the present disclosure.

FIG. 9 shows facies association and the calibration of predicted facieswith core facies, according to some implementations of the presentdisclosure.

FIG. 10 is a block diagram illustrating an example computer system usedto provide computational functionalities associated with describedalgorithms, methods, functions, processes, flows, and procedures asdescribed in the present disclosure, according to some implementationsof the present disclosure.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the implementationsillustrated in the drawings, and specific language will be used todescribe the same. Nevertheless, no limitation of the scope of thedisclosure is intended. Any alterations and further modifications to thedescribed devices, systems, methods, and any further application of theprinciples of the present disclosure are fully contemplated as wouldnormally occur to one skilled in the art to which the disclosurerelates. In particular, it is fully contemplated that the features,components, steps, or any combination thereof described with respect toone implementation may be combined with the features, components, and/orsteps described with respect to other implementations of the presentdisclosure.

The present disclosure is directed to methods and systems for performingquality control on a set of log data using elastic facies. Moreparticularly, the present disclosure is directed to using elastic faciesdata for selected or key wells to perform quality control on other wellswithin a zone of interest. This quality control process involves usingthe log data of the key wells to identify rock types in log dataobtained from other non-key wells within the zone of interest. Thepresent disclosure provides an automated process that provides foridentifying rock types in non-key wells of a zone of interest using ashortened time period compared to the labor-intensive conventionalapproach and without the need for well markers or petrophysical analysisof the non-key wells.

Conventional approaches for performing quality control of log data for aplurality of wells involve drawbacks. For example, well marker data andthe petrophysical analysis data are usually not available for all wellswithin an area of interest when Log Quality Control (“LQC”) takes place.Well marker data can include data provided from well markers. Wellmarkers can provide a data signature that is recognizable in the logdata and which can provide assistance for determining the composition ofthe environment around and in the well. Moreover, in instances wherewell marker data are available, inconsistencies in the well marker datacan lead errors in the LQC process. Conventional approaches also involvesignificant amounts of time, particularly when the LQC is associatedwith large or mega projects that involve hundreds or thousands of wells.Each of the well data associated with each of these wells needs to bereviewed. As a result, considerable amounts of time are needed, which isproblematic due to time constraints. As a result, conventionalapproaches become a bottleneck.

The methods and systems described in the present disclosure provide anefficient, consistent, and, in some implementations, an automatedapproach that markedly reduces the time required to LQC well log data,particularly log data associated with large numbers of wells. Further,the methods and systems of the present disclosure do not require theexistence of well marker data or petrophysical analysis of non-keywells, reducing the amount of data to be processed. In someimplementations, the processing time for LQC can be reducedproportionally to a fraction representing the amount of well log dataprocessed with respect to the total well log data of an environment. Forexample, if one of every two well logs for an environment are selected,the processing time for LQC can be reduced by half. This allows only thebest log data in an environment to be selected. If more well data areselected, the accuracy of the predicted facies increases. Therefore, thesystems and methods of the present disclosure accelerate the LQCprocess.

FIG. 1 is a flowchart of an example process 100 for accelerating an LQCprocess according to some implementations of the present disclosure. Atstep 102, key wells of a plurality of wells being analyzed, such as aplurality of wells disposed in a selected area or as part of acollection of related wells (such as a collection of wells in a projectarea), are selected. Key wells may be selected based on the quality ofdata associated with the well. For example, wells having accurate welldata, may be selected to be key wells. Wells, such as cored wells andwells logged with latest tools, may be selected. Quality log dataobtained by a quality logging suite and having log coverage with adesired level of penetration may be utilized in selecting key wells. Forexample, a quality logging suite can include resistivity logs,spontaneous potential (SP) logs, gamma ray (GR) logs, neutron logs,density logs, and sonic logs. The quality of a logging suite can bedetermined based on the coverage of the logging suite across one or moreof these spectra and the resolution of each log of the suite. The keywells are used as a standard for determining facies which are applied tologging data of non-key wells in order to provide LQC.

At step 104, a reference model is constructed using the data associatedwith the key wells. The reference model may be created using elasticfacies log data. Data, such as well log data, and other types of dataassociated with the key well may be used. Example data types includeRHOB (density), DT-P (P-Sonic), DT-S(Shear Sonic), GR (Gamma Ray),lithology, and neutron porosity data. Selection of the data to be usedis important, because the non-key wells should have associated data ofthe same types as those taken from the key wells and used to constructthe reference model. Generally, RHOB, DT-P, DT-S, and GR data arecollected during well logging. Therefore, in some implementations, thesedata may be used to build the reference model.

The selected well log data are used to predict the elastic facies orrock characteristics of the different subterranean rock types throughwhich a well passes. The elastic facies are predicted using astatistical analysis. In some implementations, a Principal ComponentAnalysis (PCA) technique may be used to determine the subterranean rocktypes using the key well data. Other types of data analysis may be used.For example, statistical approaches using fuzzy logic, neural networks,or multi-linear regression may also be used to predict the elasticfacies. For the purposes of illustration only, PCA is described in thefollowing description with the understanding that other types ofstatistical analysis may be used.

Data from each key well may be subjected to a separate PCA analysis. Theselected log data are used as an input to the PCA analysis, and a datacluster chart is produced for each key well. Consequently, with each keywell is used as a separate analysis, a plurality of results are obtainedto identify the facies and, ultimately, the subterranean rock types.

At step 106, the reference model is propagated to the non-key wells todetermine the subterranean rock types indicated by the associated welllog data. At step 108, well log data associated with the non-key wellsis benchmarked, and, at 110, the non-key well data is calibrated. Thatis, at step 110, a determination is made as to whether log dataassociated with the non-key wells compares favorably or unfavorably withthe reference model. A favorable determination exists where the non-keywell log data fall within a range of values used to identify aparticular type of rock. The data compares unfavorably when there is nosuch correspondence between the non-key well data and the referencemodel.

FIG. 2 is an example data cluster chart 200 and associated key 202 thatare the result of the PCA analyses. As shown, the data cluster chart 200contains five clusters of data, 204, 206, 208, 210, and 212. As is shownin FIG. 2, the clusters are separate from one another with little or anyoverlap. The PCA analyses also determine mean values 214, 216, 218, 220,and 222 for each of the clusters 204, 206, 208, 210, and 212,respectively. These different clusters represent different rock typescontained in separate resolvable zones. While some overlap between dataclusters may occur, as shown in FIG. 2, the mean values for each clusteris distinct from the means values of the other clusters. With distinctmean values and with separate clusters having little or no overlap, theresulting reference model is a satisfactory model.

The key 202 indicates five elastic facies 224, 226, 228, 230, and 232,each color-coded with the clusters identified in chart 200. Thus, theelastic facies 224, 226, 228, 230, and 232 correspond to the clusters204, 206, 208, 210, and 212, respectively. Each of these elastic faciesand, hence, each of these clusters represents a different rock type.Once optimum results are obtained via the PCA analyses, the final faciesare verified using multiple cross-plots with different elasticattributes in order to verify that the predicted facies comport withregional geological framework.

In some implementations, obtaining results that define differentclusters may be an iterative process. For example, the quality of theinput data directly affect the quality of the PCA results. Additionally,the number of expected rock types may also affect the output of the PCAanalysis. While the chart 200 shows five separate clusters, otheranalyses may resolve fewer or additional clusters.

As explained earlier, the Critical Path Analysis (CPA) identifies anumber of resolvable data clusters. Each cluster represents a particularelastic facie and, thus, a particular rock type. The CPA also identifieda mean value for each data type of the input data associated with aparticular elastic facie. Thus, for each elastic facie predicted by theCPA, a mean value of each input data type corresponding to the elasticfacie is also determined. In some implementations, a range of values foreach mean value may also be identified. That is, for a particular meanvalue, an associated upper limit and lower limit may also be identified.The upper and lower limits define intervals for each data type for aparticular predicted elastic facie. Thus, a predicted elastic facie hasa combination of particular values of the input data that distinguishthis particular elastic facie from the others.

FIGS. 3A-3B include an example chart 300 that shows well log data 302input into a CPA and a continuous facies output 304 from the CPA and across-plot 308. In this particular example, five data types are used asinput data for the CPA. Particularly, RHOB, DT, GR, neutron (NEUT), anddelta-time shear wave velocity (DTSM) data are used. As mentioned abovemore, fewer, or different data types may be used. The elastic faciesoutput 304 identify different rock types 312 a-e encountered during welllogging and predicted as a result of the CPA. Again, these differentrock types 312 a-e are reflected in the key 306. These rock types 312a-e are verified with the use of cross-plots, such as cross-plot 308.Different cross-plots may be obtained with each different CPA. Theexample cross-plot 308 is a primary wave velocity and secondary wavevelocity ratio (Vp/Vs ratio) versus acoustic impedance (AI) plot. Othertypes of cross-plots may be used to verify the rock types represented bythe CPA output.

In addition to identifying the different subterranean rock typesencountered, this process also includes the added benefit of identifyinghydrocarbon reservoirs or sweet spots 310. These areas are detected asareas of reduced density as a result of the presence of hydrocarbons.

With the elastic facies and, hence, rock types verified, each elasticfacie is assigned a value having a selected upper and lower range ofvalues. The value assigned to each of the elastic facies, and, thus,rock types, is the mean value determined by the CPA for each elasticfacie. A range of values around this mean value is also selected. Thus,the mean value is given an upper range and a lower range. Duringcomparison with the log data of the non-key wells, if the each of thedata values fall within the respective ranges for each of the datatypes, then a rock type is identified using the reference model.

The reference model is propagated to the non-key well data, as shown at106. By propagating the reference model to the non-key well data, theelastic facies and, hence, rock types associated with the well log dataare quickly identified or marked as needing additional review. Asmentioned above, each elastic facie has associated numeric intervals foreach data type used to generate the reference model. The reference modelis propagated or applied to the log data of the non-key wells.

Once propagated, the log data of the non-key wells is compared to thedetermined data intervals associated with a particular elastic facie tothe well log data associated with a non-key well, which representsbenchmarking, which corresponds to 108 explained above with respect toFIG. 1. If the well log data of a non-key well falls within thedetermined intervals for a particular point of measurement, then a rocktype is identified. If there is no match with any of the elastic faciescontained in the reference model, then a notation may be made that nomatch exists and that these particular data may need to be revisited. Asmentioned earlier, the non-key wells should at least have the well logdata types used to generate the reference model. For example, whereRHOB, DT-C, DT-S, and GR data were used to generate the reference model,each of the non-key wells should have at least this data in order for acomparison to occur. A “badhole” flag, that is, a differential caliper(DCAL), or bulk density correction (DRHO) may be used to flag washoutszones and to exclude such intervals during benchmarking. Identifying ornot identifying correspondence between the elastic facies intervals withthe non-key well data during benchmarking represents a calibration, asdescribed earlier in 110 of FIG. 1.

FIGS. 4A-4B show a facies comparison with petrophysical analyses fornon-key wells. FIGS. 4A-4B show propagation of the reference model tothe non-key wells, as previously described in relation to FIG. 1. Thereference model is propagated to non-key wells 402, 404, 406, and 408 topredict the facies for the non-key wells. The non-key wells 402, 404,406, 408 should include at least the minimum set of logs required to runthe model. In some implementations, a badhole flag e.g., DCAL(differential caliper) or DRHO (bulk density correction) is used to flagthe washouts zones to exclude these intervals during the benchmarkinglog readings stage.

FIGS. 5A-5B show a series of histograms for selected key wells for datatypes DT-P 502, DTSM 504, GR 506, and RHOB 508, respectively. As shownin FIG. 5, the mean value for DT-P 502 is 69.95 μs/ft.; the mean valuefor DTSM is 137 μs/ft.; the mean value for GR is 35 gapi; and the meanvalue for RHOB is 2.58 g/cc. As explained above, with the use of the keywell data, a mean value and interval for each data type correspondingfor a particular elastic facie is generated.

FIG. 6 shows histograms 602, 604, 606, 608, 610, and 612 for DT-P datafor six different non-key wells. The system is configured for thecalibration of the non-key wells. For example, the mean value from eachindividual non-key well can be compared to the benchmark value describedin relation to FIG. 5. The system is configured to flag any outlier. Forexample, as shown in FIG. 6, if the value for DT-P was 69.96+/−1.5μs/ft., and if this DT-P interval is compared to the different valuesfor DT-P for the different non-key wells, all of the values fall withinthe range except for the non-key well associated with histogram 612representing well 6, where the DT-P value is 87 μs/ft. By using theexample method presented in FIG. 1, the LQC process for a group of wellsmay be accomplish in less time compared to conventional approaches andwith less data, such as without well marker data and withoutpetrophysical analyses.

FIGS. 7A-7C show a final predicted facies for a key well in a studyarea. The rectangles 702, 704, and 706 on the well logs represent gasstringers. A density-neutron cross-plot in FIG. 7B and a Vp/Vs ratioversus AI cross-plot in FIG. 7C are also shown. The cross-plots shows acluster of points identified by ellipses 708 and 710, respectively,associated with the gas stringers. Multi-well cross-plots with differentattributes may alert a user to pay attention while conditioning orde-spiking logs over gas zones. This is just one example to show theadditional benefits of the described systems and methods.

FIGS. 8A-8D show the Vp/Vs ratio versus AI cross-plots color coded withdifferent attributes, shown in graphs 802, 804, 806, and 808,respectively. The system validates the predicted facies for the non-keywells.

FIG. 9 shows facies association and the calibration of predicted facieswith core facies. Once the facies are calibrated, these facies may beused with inverted seismic data for prospects generation. For example,in FIG. 9, the labeled elastic facies 902 are each associated with acore description 904. Table 906 shows an example of relative frequencies(%) for each reference rock type. The predicted elastic facies 908 whencalibrated with core facies, can be used with inverted seismic data as aguiding tool for prospects generation to make informed decisions.

The present disclosure reduces or eliminates inconsistencies in the LQCprocess and calibrations of non-key well data by providing an accurateand consistent approach. The present disclosure eliminates the need forwell markers or petrophysical analyses for non-key wells in order toaccomplish the LQC process. The present disclosure reduces the timerequired to perform an LQC process for a large collection of wellsassociated with a project with tight deadlines, such as mega projectsthat may encompass hundreds or thousands of wells. The presentdisclosure provides an accurate and consistent approach to calibratelogs across a field, enabling the generation high quality log data forrock physics and seismic inversion studies. The marker data and thepetrophysical analysis usually not available for all wells at the timeof LQC.

FIG. 10 is a block diagram of an example computer system 1000 used toprovide computational functionalities associated with describedalgorithms, methods, functions, processes, flows, and proceduresdescribed in the present disclosure, according to some implementationsof the present disclosure. The illustrated computer 1002 is intended toencompass any computing device such as a server, a desktop computer, alaptop/notebook computer, a wireless data port, a smart phone, apersonal data assistant (PDA), a tablet computing device, or one or moreprocessors within these devices, including physical instances, virtualinstances, or both. The computer 1002 can include input devices such askeypads, keyboards, and touch screens that can accept user information.Also, the computer 1002 can include output devices that can conveyinformation associated with the operation of the computer 1002. Theinformation can include digital data, visual data, audio information, ora combination of information. The information can be presented in agraphical user interface (UI) (or GUI).

The computer 1002 can serve in a role as a client, a network component,a server, a database, a persistency, or components of a computer systemfor performing the subject matter described in the present disclosure.The illustrated computer 1002 is communicably coupled with a network1030. In some implementations, one or more components of the computer1002 can be configured to operate within different environments,including cloud-computing-based environments, local environments, globalenvironments, and combinations of environments.

At a high level, the computer 1002 is an electronic computing deviceoperable to receive, transmit, process, store, and manage data andinformation associated with the described subject matter. According tosome implementations, the computer 1002 can also include, or becommunicably coupled with, an application server, an email server, a webserver, a caching server, a streaming data server, or a combination ofservers.

The computer 1002 can receive requests over network 1030 from a clientapplication (for example, executing on another computer 1002). Thecomputer 1002 can respond to the received requests by processing thereceived requests using software applications. Requests can also be sentto the computer 1002 from internal users (for example, from a commandconsole), external (or third) parties, automated applications, entities,individuals, systems, and computers.

Each of the components of the computer 1002 can communicate using asystem bus 1003. In some implementations, any or all of the componentsof the computer 1002, including hardware or software components, caninterface with each other or the interface 1004 (or a combination ofboth), over the system bus 1003. Interfaces can use an applicationprogramming interface (API) 1012, a service layer 1013, or a combinationof the API 1012 and service layer 1013. The API 1012 can includespecifications for routines, data structures, and object classes. TheAPI 1012 can be either computer-language independent or dependent. TheAPI 1012 can refer to a complete interface, a single function, or a setof APIs.

The service layer 1013 can provide software services to the computer1002 and other components (whether illustrated or not) that arecommunicably coupled to the computer 1002. The functionality of thecomputer 1002 can be accessible for all service consumers using thisservice layer. Software services, such as those provided by the servicelayer 1013, can provide reusable, defined functionalities through adefined interface. For example, the interface can be software written inJAVA, C++, or a language providing data in extensible markup language(XML) format. While illustrated as an integrated component of thecomputer 1002, in alternative implementations, the API 1012 or theservice layer 1013 can be stand-alone components in relation to othercomponents of the computer 1002 and other components communicablycoupled to the computer 1002. Moreover, any or all parts of the API 1012or the service layer 1013 can be implemented as child or sub-modules ofanother software module, enterprise application, or hardware modulewithout departing from the scope of the present disclosure.

The computer 1002 includes an interface 1004. Although illustrated as asingle interface 1004 in FIG. 10, two or more interfaces 1004 can beused according to particular needs, desires, or particularimplementations of the computer 1002 and the described functionality.The interface 1004 can be used by the computer 1002 for communicatingwith other systems that are connected to the network 1030 (whetherillustrated or not) in a distributed environment. Generally, theinterface 1004 can include, or be implemented using, logic encoded insoftware or hardware (or a combination of software and hardware)operable to communicate with the network 1030. More specifically, theinterface 1004 can include software supporting one or more communicationprotocols associated with communications. As such, the network 1030 orthe interface's hardware can be operable to communicate physical signalswithin and outside of the illustrated computer 1002.

The computer 1002 includes a processor 1005. Although illustrated as asingle processor 1005 in FIG. 10, two or more processors 1005 can beused according to particular needs, desires, or particularimplementations of the computer 1002 and the described functionality.Generally, the processor 1005 can execute instructions and canmanipulate data to perform the operations of the computer 1002,including operations using algorithms, methods, functions, processes,flows, and procedures as described in the present disclosure.

The computer 1002 also includes a database 1006 that can hold data forthe computer 1002 and other components connected to the network 1030(whether illustrated or not). For example, database 1006 can be anin-memory, conventional, or a database storing data consistent with thepresent disclosure. In some implementations, database 1006 can be acombination of two or more different database types (for example, hybridin-memory and conventional databases) according to particular needs,desires, or particular implementations of the computer 1002 and thedescribed functionality. Although illustrated as a single database 1006in FIG. 10, two or more databases (of the same, different, orcombination of types) can be used according to particular needs,desires, or particular implementations of the computer 1002 and thedescribed functionality. While database 1006 is illustrated as aninternal component of the computer 1002, in alternative implementations,database 1006 can be external to the computer 1002.

The computer 1002 also includes a memory 1007 that can hold data for thecomputer 1002 or a combination of components connected to the network1030 (whether illustrated or not). Memory 1007 can store any dataconsistent with the present disclosure. In some implementations, memory1007 can be a combination of two or more different types of memory (forexample, a combination of semiconductor and magnetic storage) accordingto particular needs, desires, or particular implementations of thecomputer 1002 and the described functionality. Although illustrated as asingle memory 1007 in FIG. 10, two or more memories 1007 (of the same,different, or combination of types) can be used according to particularneeds, desires, or particular implementations of the computer 1002 andthe described functionality. While memory 1007 is illustrated as aninternal component of the computer 1002, in alternative implementations,memory 1007 can be external to the computer 1002.

The application 1008 can be an algorithmic software engine providingfunctionality according to particular needs, desires, or particularimplementations of the computer 1002 and the described functionality.For example, application 1008 can serve as one or more components,modules, or applications. Further, although illustrated as a singleapplication 1008, the application 1008 can be implemented as multipleapplications 1008 on the computer 1002. In addition, althoughillustrated as internal to the computer 1002, in alternativeimplementations, the application 1008 can be external to the computer1002.

The computer 1002 can also include a power supply 1014. The power supply1014 can include a rechargeable or non-rechargeable battery that can beconfigured to be either user- or non-user-replaceable. In someimplementations, the power supply 1014 can include power-conversion andmanagement circuits, including recharging, standby, and power managementfunctionalities. In some implementations, the power-supply 1014 caninclude a power plug to allow the computer 1002 to be plugged into awall socket or a power source to, for example, power the computer 1002or recharge a rechargeable battery.

There can be any number of computers 1002 associated with, or externalto, a computer system containing computer 1002, with each computer 1002communicating over network 1030. Further, the terms “client,” “user,”and other appropriate terminology can be used interchangeably, asappropriate, without departing from the scope of the present disclosure.Moreover, the present disclosure contemplates that many users can useone computer 1002 and one user can use multiple computers 1002.

Implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Software implementations of the described subjectmatter can be implemented as one or more computer programs. Eachcomputer program can include one or more modules of computer programinstructions encoded on a tangible, non-transitory, computer-readablecomputer-storage medium for execution by, or to control the operationof, data processing apparatus. Alternatively, or additionally, theprogram instructions can be encoded in/on an artificially generatedpropagated signal. The example, the signal can be a machine-generatedelectrical, optical, or electromagnetic signal that is generated toencode information for transmission to suitable receiver apparatus forexecution by a data processing apparatus. The computer-storage mediumcan be a machine-readable storage device, a machine-readable storagesubstrate, a random or serial access memory device, or a combination ofcomputer-storage mediums.

The terms “data processing apparatus,” “computer,” and “electroniccomputer device” (or equivalent as understood by one of ordinary skillin the art) refer to data processing hardware. For example, a dataprocessing apparatus can encompass all kinds of apparatus, devices, andmachines for processing data, including by way of example, aprogrammable processor, a computer, or multiple processors or computers.The apparatus can also include special purpose logic circuitryincluding, for example, a central processing unit (CPU), a fieldprogrammable gate array (FPGA), or an application specific integratedcircuit (ASIC). In some implementations, the data processing apparatusor special purpose logic circuitry (or a combination of the dataprocessing apparatus or special purpose logic circuitry) can behardware- or software-based (or a combination of both hardware- andsoftware-based). The apparatus can optionally include code that createsan execution environment for computer programs, for example, code thatconstitutes processor firmware, a protocol stack, a database managementsystem, an operating system, or a combination of execution environments.The present disclosure contemplates the use of data processingapparatuses with or without conventional operating systems, for exampleLINUX, UNIX, WINDOWS, MAC OS, ANDROID, or IOS.

A computer program, which can also be referred to or described as aprogram, software, a software application, a module, a software module,a script, or code, can be written in any form of programming language.Programming languages can include, for example, compiled languages,interpreted languages, declarative languages, or procedural languages.Programs can be deployed in any form, including as stand-alone programs,modules, components, subroutines, or units for use in a computingenvironment. A computer program can, but need not, correspond to a filein a file system. A program can be stored in a portion of a file thatholds other programs or data, for example, one or more scripts stored ina markup language document, in a single file dedicated to the program inquestion, or in multiple coordinated files storing one or more modules,sub programs, or portions of code. A computer program can be deployedfor execution on one computer or on multiple computers that are located,for example, at one site or distributed across multiple sites that areinterconnected by a communication network. While portions of theprograms illustrated in the various figures may be shown as individualmodules that implement the various features and functionality throughvarious objects, methods, or processes, the programs can instead includea number of sub-modules, third-party services, components, andlibraries. Conversely, the features and functionality of variouscomponents can be combined into single components as appropriate.Thresholds used to make computational determinations can be statically,dynamically, or both statically and dynamically determined.

The methods, processes, or logic flows described in this specificationcan be performed by one or more programmable computers executing one ormore computer programs to perform functions by operating on input dataand generating output. The methods, processes, or logic flows can alsobe performed by, and apparatus can also be implemented as, specialpurpose logic circuitry, for example, a CPU, an FPGA, or an ASIC.

Computers suitable for the execution of a computer program can be basedon one or more of general and special purpose microprocessors and otherkinds of CPUs. The elements of a computer are a CPU for performing orexecuting instructions and one or more memory devices for storinginstructions and data. Generally, a CPU can receive instructions anddata from (and write data to) a memory. A computer can also include, orbe operatively coupled to, one or more mass storage devices for storingdata. In some implementations, a computer can receive data from, andtransfer data to, the mass storage devices including, for example,magnetic, magneto optical disks, or optical disks. Moreover, a computercan be embedded in another device, for example, a mobile telephone, apersonal digital assistant (PDA), a mobile audio or video player, a gameconsole, a global positioning system (GPS) receiver, or a portablestorage device such as a universal serial bus (USB) flash drive.

Computer readable media (transitory or non-transitory, as appropriate)suitable for storing computer program instructions and data can includeall forms of permanent/non-permanent and volatile/nonvolatile memory,media, and memory devices. Computer readable media can include, forexample, semiconductor memory devices such as random access memory(RAM), read only memory (ROM), phase change memory (PRAM), static randomaccess memory (SRAM), dynamic random access memory (DRAM), erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), and flash memory devices.Computer readable media can also include, for example, magnetic devicessuch as tape, cartridges, cassettes, and internal/removable disks.Computer readable media can also include magneto optical disks andoptical memory devices and technologies including, for example, digitalvideo disc (DVD), CD ROM, DVD+/−R, DVD-RAM, DVD-ROM, HD-DVD, and BLURAY.The memory can store various objects or data, including caches, classes,frameworks, applications, modules, backup data, jobs, web pages, webpage templates, data structures, database tables, repositories, anddynamic information. Types of objects and data stored in memory caninclude parameters, variables, algorithms, instructions, rules,constraints, and references. Additionally, the memory can include logs,policies, security or access data, and reporting files. The processorand the memory can be supplemented by, or incorporated in, specialpurpose logic circuitry.

Implementations of the subject matter described in the presentdisclosure can be implemented on a computer having a display device forproviding interaction with a user, including displaying information to(and receiving input from) the user. Types of display devices caninclude, for example, a cathode ray tube (CRT), a liquid crystal display(LCD), a light-emitting diode (LED), and a plasma monitor. Displaydevices can include a keyboard and pointing devices including, forexample, a mouse, a trackball, or a trackpad. User input can also beprovided to the computer through the use of a touchscreen, such as atablet computer surface with pressure sensitivity or a multi-touchscreen using capacitive or electric sensing. Other kinds of devices canbe used to provide for interaction with a user, including to receiveuser feedback including, for example, sensory feedback including visualfeedback, auditory feedback, or tactile feedback. Input from the usercan be received in the form of acoustic, speech, or tactile input. Inaddition, a computer can interact with a user by sending documents to,and receiving documents from, a device that is used by the user. Forexample, the computer can send web pages to a web browser on a user'sclient device in response to requests received from the web browser.

The term “graphical user interface,” or “GUI,” can be used in thesingular or the plural to describe one or more graphical user interfacesand each of the displays of a particular graphical user interface.Therefore, a GUI can represent any graphical user interface, including,but not limited to, a web browser, a touch screen, or a command lineinterface (CLI) that processes information and efficiently presents theinformation results to the user. In general, a GUI can include aplurality of user interface (UI) elements, some or all associated with aweb browser, such as interactive fields, pull-down lists, and buttons.These and other UI elements can be related to or represent the functionsof the web browser.

Implementations of the subject matter described in this specificationcan be implemented in a computing system that includes a back endcomponent, for example, as a data server, or that includes a middlewarecomponent, for example, an application server. Moreover, the computingsystem can include a front-end component, for example, a client computerhaving one or both of a graphical user interface or a Web browserthrough which a user can interact with the computer. The components ofthe system can be interconnected by any form or medium of wireline orwireless digital data communication (or a combination of datacommunication) in a communication network. Examples of communicationnetworks include a local area network (LAN), a radio access network(RAN), a metropolitan area network (MAN), a wide area network (WAN),Worldwide Interoperability for Microwave Access (WIMAX), a wirelesslocal area network (WLAN) (for example, using 802.11 a/b/g/n or 802.20or a combination of protocols), all or a portion of the Internet, or anyother communication system or systems at one or more locations (or acombination of communication networks). The network can communicatewith, for example, Internet Protocol (IP) packets, frame relay frames,asynchronous transfer mode (ATM) cells, voice, video, data, or acombination of communication types between network addresses.

The computing system can include clients and servers. A client andserver can generally be remote from each other and can typicallyinteract through a communication network. The relationship of client andserver can arise by virtue of computer programs running on therespective computers and having a client-server relationship.

Cluster file systems can be any file system type accessible frommultiple servers for read and update. Locking or consistency trackingmay not be necessary since the locking of exchange file system can bedone at application layer. Furthermore, Unicode data files can bedifferent from non-Unicode data files.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of what may beclaimed, but rather as descriptions of features that may be specific toparticular implementations. Certain features that are described in thisspecification in the context of separate implementations can also beimplemented, in combination, in a single implementation. Conversely,various features that are described in the context of a singleimplementation can also be implemented in multiple implementations,separately, or in any suitable sub-combination. Moreover, althoughpreviously described features may be described as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can, in some cases, be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

Particular implementations of the subject matter have been described.Other implementations, alterations, and permutations of the describedimplementations are within the scope of the following claims as will beapparent to those skilled in the art. While operations are depicted inthe drawings or claims in a particular order, this should not beunderstood as requiring that such operations be performed in theparticular order shown or in sequential order, or that all illustratedoperations be performed (some operations may be considered optional), toachieve desirable results. In certain circumstances, multitasking orparallel processing (or a combination of multitasking and parallelprocessing) may be advantageous and performed as deemed appropriate.

Moreover, the separation or integration of various system modules andcomponents in the previously described implementations should not beunderstood as requiring such separation or integration in allimplementations, and it should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products.

Accordingly, the previously described example implementations do notdefine or constrain the present disclosure. Other changes,substitutions, and alterations are also possible without departing fromthe spirit and scope of the present disclosure.

Furthermore, any claimed implementation is considered to be applicableto at least a computer-implemented method; a non-transitory,computer-readable medium storing computer-readable instructions toperform the computer-implemented method; and a computer systemcomprising a computer memory interoperably coupled with a hardwareprocessor configured to perform the computer-implemented method or theinstructions stored on the non-transitory, computer-readable medium.

A number of embodiments of the present disclosure have been described.Nevertheless, it will be understood that various modifications may bemade without departing from the spirit and scope of the presentdisclosure. Accordingly, other embodiments are within the scope of thefollowing claims.

1. A method of identifying elastic facies in non-key wells as part ofLog Quality Control (LQC) comprising: selecting one or more key wells;building a reference model of elastic facies using well log data of theselected one or more key wells; propagating the reference model to welldata of one or more non-key wells; benchmarking the well data of thenon-key wells with the reference model; and calibrating the well data ofthe non-key wells with the reference model.
 2. The method of claim 1,wherein building the reference model comprises performing a principalComponent Analysis (PCA) on the well log data of the one or more keywells.
 3. The method of claim 1, wherein the one or more key wells areselected according to one or more of: a level of penetration of the welllog data, a period of time in which the well log data is captured, aresolution of the well log data, a reservoir coverage of the well logdata, and an areal coverage representing lithological variations withina well being represented by the well log data.
 4. The method of claim 1,wherein building the reference model comprises predicting elastic faciesof a key well.
 5. The method of claim 4, further comprising: validatingthe predicted elastic facies using a plurality of cross-plots, eachcross-plot including different elastic attributes from other cross-plotsof the plurality.
 6. The method of claim 4, wherein each elastic faciesis represented by a data cluster from a critical path analysis (CPA),and wherein the CPA is further configured to provide, for each elasticfacies, a mean value of each input data type corresponding to thatelastic facie to distinguish that elastic facie from other elasticfacies.
 7. The method of claim 1, comprising, in response to calibratingthe well data of the non-key wells with the reference model, generatingan alert when the well data of a non-key well is outside of a range ofvalues indicated by the reference model.
 8. A system for identifyingelastic facies in non-key wells as part of Log Quality Control (LQC)comprises: a memory for storing one or more instructions; one or moreprocessing devices in communication with the memory and configured toexecute the one or more instructions to perform operations comprising:selecting one or more key wells; building a reference model of elasticfacies using well log data of the selected one or more key wells;propagating the reference model to well data of one or more non-keywells; benchmarking the well data of the non-key wells with thereference model; and calibrating the well data of the non-key wells withthe reference model.
 9. The system of claim 8, wherein building thereference model comprises performing a principal Component Analysis(PCA) on the well log data of the one or more key wells.
 10. The systemof claim 8, wherein the one or more key wells are selected according toone or more of: a level of penetration of the well log data, a period oftime in which the well log data is captured, a resolution of the welllog data, a reservoir coverage of the well log data, and an arealcoverage representing lithological variations within a well beingrepresented by the well log data.
 11. The system of claim 8, whereinbuilding the reference model comprises predicting elastic facies of akey well.
 12. The system of claim 11, wherein the operations furthercomprise: validating the predicted elastic facies using a plurality ofcross-plots, each cross-plot including different elastic attributes fromother cross-plots of the plurality.
 13. The system of claim 11, whereineach elastic facies is represented by a data cluster from a criticalpath analysis (CPA), and wherein the CPA is further configured toprovide, for each elastic facies, a mean value of each input data typecorresponding to that elastic facie to distinguish that elastic faciefrom other elastic facies.
 14. The system of claim 8, wherein theoperations further comprise: generating, in response to calibrating thewell data of the non-key wells with the reference model, an alert whenthe well data of a non-key well is outside of a range of valuesindicated by the reference model.
 15. One or more non-transitorycomputer readable media storing instructions that are executable by oneor more processors configured to perform operations for identifyingelastic facies in non-key wells as part of Log Quality Control (LQC),the operations comprising: selecting one or more key wells; building areference model of elastic facies using well log data of the selectedone or more key wells; propagating the reference model to well data ofone or more non-key wells; benchmarking the well data of the non-keywells with the reference model; and calibrating the well data of thenon-key wells with the reference model.
 16. The one or morenon-transitory computer readable media of claim 15, wherein building thereference model comprises performing a principal Component Analysis(PCA) on the well log data of the one or more key wells.
 17. The one ormore non-transitory computer readable media of claim 15, wherein the oneor more key wells are selected according to one or more of: a level ofpenetration of the well log data, a period of time in which the well logdata is captured, a resolution of the well log data, a reservoircoverage of the well log data, and an areal coverage representinglithological variations within a well being represented by the well logdata.
 18. The one or more non-transitory computer readable media ofclaim 15, wherein building the reference model comprises predictingelastic facies of a key well.
 19. The one or more non-transitorycomputer readable media of claim 18, further comprising: validating thepredicted elastic facies using a plurality of cross-plots, eachcross-plot including different elastic attributes from other cross-plotsof the plurality.
 20. The one or more non-transitory computer readablemedia of claim 18, wherein each elastic facies is represented by a datacluster from a critical path analysis (CPA), and wherein the CPA isfurther configured to provide, for each elastic facies, a mean value ofeach input data type corresponding to that elastic facie to distinguishthat elastic facie from other elastic facies.